IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v18y1993i3p245-251.html
   My bibliography  Save this article

Maximum likelihood estimation in branching process with continuous state space

Author

Listed:
  • Kale, Mohan M.
  • Deshmukh, S. R.

Abstract

In some biological studies it is impossible to count the total number of individuals but the size of the population can be measured by its weight or volume. The numerical growth of such populations can be modelled by a branching process with state space [0, [infinity]). In this paper estimation of the mean of the offspring distribution of such a process is studied when the subordinator process is assumed to be the gamma process. The asymptotic properties of the estimator are established. It is also shown that the branching process with state space [0, [infinity]) when the corresponding subordinator process is a gamma process is a non-ergodic model and it belongs to the curved exponential family. The curvature of the model is also obtained.

Suggested Citation

  • Kale, Mohan M. & Deshmukh, S. R., 1993. "Maximum likelihood estimation in branching process with continuous state space," Statistics & Probability Letters, Elsevier, vol. 18(3), pages 245-251, October.
    • Handle: RePEc:eee:stapro:v:18:y:1993:i:3:p:245-251
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0167-7152(93)90224-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:18:y:1993:i:3:p:245-251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.